Authors:
Shaufikah Shukri
1
;
Latifah Munirah Kamarudin
1
;
David Lorater Ndzi
2
;
Ammar Zakaria
3
;
Saidatul Norlyna Azemi
4
;
Kamarulzaman Kamarudin
3
and
Syed Muhammad Mamduh Syed Zakaria
5
Affiliations:
1
School of Computer and Communication Engineering, University Malaysia Perlis, Centre of Excellence for Advanced Sensor and Technology and University Malaysia Perlis, Malaysia
;
2
School of Engineering and Computing and University of the West of Scotland, United Kingdom
;
3
Centre of Excellence for Advanced Sensor and Technology, University Malaysia Perlis, School of Microelectronic Engineering and University Malaysia Perlis, Malaysia
;
4
School of Computer and Communication Engineering and University Malaysia Perlis, Malaysia
;
5
Centre of Excellence for Advanced Sensor and Technology and University Malaysia Perlis, Malaysia
Keyword(s):
Device-free Localization, Elderly Care Application, Indoor Detection, IoT, RSSI, WSN.
Related
Ontology
Subjects/Areas/Topics:
Data Communication Networking
;
Enterprise Information Systems
;
Internet of Things
;
Sensor Networks
;
Software Agents and Internet Computing
;
Software and Architectures
;
Telecommunications
Abstract:
Device-Free Localization (DFL) is an effective human localizing system that exploits changes in radio signals strength of radio network. DFL is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. DFL is ideal for monitoring the elderly activities without causing any physical discomfort with the wearable devices. It is challenging for elderly to remember each day to wear or to activate those devices. The purpose of this study is to select the best DFL methods in term of detection and tracking accuracy, which is suitable for human monitoring application especially for elderly and disable people. This paper proposes an RSSI-based DFL system that can be used to detect and locate elderly people in an area of interest (AoI) using changes in signal strength measurements. An attenuation-based and variance based methods have been introduced in the proposed DFL system. In stationary people scenario, attenuation-based method managed to accura
tely detect the presence of human, which is very suitable for elderly care application compared to variance-based DFL. The result shows that attenuation-based method managed to detect all trajectories of moving people with 100% detection accuracy while variance-based method only give 71.74% accuracy.
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